AI Strategy and AutomationProposed offering

Replace scattered AI experiments with one roadmap your teams will actually follow.

We define the operating model, governance, architecture and adoption plan that move AI from pilots in separate departments to a managed capability with owners, budgets and measures.

Plan your AI roadmap See a roadmap in action

This is a service Kindlebit proposes to deliver. No named customer project is published for it on this site.

Roadmap layers

Outcomes
Operating model
Governance
Platform
Adoption
Measures

Reference design. Components are options, not a statement of what is deployed at any customer.

Problems this service is built to solve

These are situations we expect buyers to recognise. Each one states why it happens, what it costs and how we would approach it.

Fragmented departmental AI initiatives

Sales buys a meeting summariser, support builds its own bot on a different vendor, and HR trials a screening tool. Each has separate contracts, separate data copies and a separate security review, and none share components.

Why it happens: Without a central view, every team solves the same plumbing problem alone and chooses tools for its own convenience.
What it costs: Duplicate spend, inconsistent controls and a growing list of vendors that security cannot review fast enough.
How we approach it: We inventory current initiatives, group them by shared capability such as retrieval, identity and logging, and define a reference platform and a lightweight intake process so teams reuse components instead of rebuilding them.
What to measure: Number of AI vendors and platforms in use, share of projects using shared components, and duplicate spend identified.

Poor employee adoption

A copilot was rolled out to 800 staff with a single training session. Three months later a small group uses it daily and most of the rest tried it once and returned to their old habits.

Why it happens: The tool was introduced as a feature, not as a change to a specific job. Nobody redesigned the task, removed the old step or agreed what good use looks like.
What it costs: Licences are paid for and unused, productivity claims cannot be proved, and sceptics gain evidence that AI does not help.
How we approach it: We pick the roles and tasks where the tool saves measurable effort, redesign the task around it, train in context with real examples, and appoint champions with a feedback loop to product and engineering.
What to measure: Weekly active use by role, task completion time before and after, and the share of users who report the tool saved time.

Unclear implementation ownership

The data team says AI belongs to the business, the business says it belongs to IT, and the risk team reviews things after the fact. A production assistant gives a wrong answer and nobody is sure who owns the fix.

Why it happens: AI cuts across data, application, security and operations, and the organisation chart does not.
What it costs: Decisions queue, incidents linger and projects either stall or proceed without accountability.
How we approach it: We define roles for product owners, platform engineering, risk, and model operations, write a decision rights matrix, and set an escalation path for model incidents.
What to measure: Time to decision on intake requests, incident resolution time, and the share of live AI systems with a named owner.

Uncontrolled AI operating costs

A pilot that cost little to demonstrate now handles thousands of requests a day. The monthly model bill has tripled and nobody can attribute it to teams or features.

Why it happens: Usage based pricing grows with adoption, and prompts and context sizes tend to expand over time without review.
What it costs: Budget overruns, sudden restrictions on use, and a loss of trust in the business case.
How we approach it: We design cost visibility into the platform: per team and per feature metering, budgets and alerts, model routing so cheaper models handle simpler tasks, caching and context limits.
What to measure: Cost per request and per completed task, budget variance by team, and the share of traffic routed to lower cost models without quality loss.

Solutions we engineer

Concrete capabilities, each with the need it serves, how it integrates, what you receive and the value to expect.

Transformation roadmap

Waves of initiatives ordered by value and dependency, with entry and exit criteria for each wave.

Customer need: A sequenced plan leadership can fund in stages.
Integration: Aligned to your planning and budget cycle.
Deliverable: Eighteen month roadmap with funding gates.
Business value: Predictable investment and early evidence.

AI operating model and decision rights

Roles, forums and decision rights for intake, build, risk review and run, sized to your organisation.

Customer need: Make ownership explicit.
Integration: Your existing governance bodies and delivery methods.
Deliverable: Operating model document and RACI.
Business value: Faster decisions and fewer stalled projects.

Governance and policy framework

Policies for data use, model approval, human oversight, third party tools and incident handling, mapped to NIST AI RMF functions.

Customer need: Safe use without blocking progress.
Integration: Security, privacy, legal and procurement.
Deliverable: Policy set and approval workflow.
Business value: Consistent controls across teams.

Reference architecture and platform plan

A target architecture covering model gateway, retrieval, identity, logging, evaluation and cost metering, with build versus buy choices.

Customer need: Stop teams rebuilding the same plumbing.
Integration: Your cloud, identity provider and data platform.
Deliverable: Architecture blueprint and platform backlog.
Business value: Reuse, lower cost and consistent security.

Change enablement and training

Role based task redesign, in context training, champions network and a feedback process.

Customer need: Move from licences to habits.
Integration: HR learning systems and team rituals.
Deliverable: Adoption plan, training material and measurement plan.
Business value: Real use, measured by role.

KPI and value tracking design

A metric tree linking each initiative to business outcomes, with instrumentation requirements.

Customer need: Prove value after launch, not only before.
Integration: Analytics and finance reporting.
Deliverable: KPI definitions, dashboard specification and review cadence.
Business value: Ongoing, evidence based investment decisions.

How we solve it

A delivery sequence that includes model selection, evaluation, data governance and human oversight.

Understand the ambition

Interview executives and domain leaders to capture outcomes, constraints and appetite for risk.

Inventory the current state

List existing initiatives, tools, contracts, data flows and skills, and the controls already in place.

Design the operating model

Define roles, forums, decision rights and intake rules sized to your organisation.

Design governance

Write policies and approval paths covering data, models, human oversight and incidents. Define evaluation requirements for each risk tier.

Plan the architecture

Choose platform components, model access patterns, data governance controls and cost metering.

Plan adoption

Select roles and tasks to redesign, define training and champions, and specify how use will be measured.

Sequence and fund

Order initiatives into waves with funding gates and success criteria.

Operate and review

Set the review cadence for metrics, cost and risk, and update the roadmap as models and regulation change.

Solution in action: Consolidating AI efforts in a multi department services firm

Reference Architecture An illustrative scenario. It describes how we would structure the work, not a delivered customer project.

Starting problem

Five departments run separate AI pilots with four vendors and no shared controls.

Existing workflow

Each team negotiates its own contract, copies data into its own tool and reports value in its own terms. Security reviews each tool separately.

Improved workflow

A central intake classifies each request by risk tier, directs it to the shared platform where possible, and applies the same logging, access control and evaluation rules. Teams keep ownership of use cases while the platform team owns the plumbing.

A department proposes a new AI use case.

Systems involved

Identity provider, data platform, model gateway, ticketing, finance reporting.

Data movement

Usage and cost telemetry from the gateway feeds a monthly review. Business metrics come from existing reports.

Human decisions

A review board approves high risk use cases. Department owners sign off on adoption targets.

Automation opportunities

Gateway enforces budgets and logs prompts and outputs under the retention policy. Intake forms route by risk tier.

Exception handling

Requests that cannot use the platform require a documented exception with an expiry date.

Resulting user experience

A team lead submits an idea, receives a risk tier and a path within days, and reuses shared components instead of procuring a new tool.

KPIs to evaluate

Time from request to approved start
Share of projects on the shared platform
Cost per completed AI task

What you receive

Concrete deliverables for this service, written so you can check them against the contract.

AI strategy document linked to business outcomes
Operating model, RACI and decision rights
Governance policies and approval workflow
Reference architecture and platform backlog
Cost model with metering and budget design
Adoption and training plan by role
KPI tree and dashboard specification
Staged roadmap with funding gates

Technology and engineering

Options we would evaluate for this service. Unless a group is marked as publicly listed on kindlebit.com, treat each tool as a proposed implementation option. Naming a tool does not imply a vendor partnership.

Platform building blocks (proposed implementation options)

Model gateway and routing
Vector and keyword search
Identity and access management
Prompt and output logging
Evaluation tooling

Cloud options

AWS
Microsoft Azure
Google Cloud

Governance references

NIST AI Risk Management Framework
OWASP Top 10 for LLM Applications

Relevant Kindlebit work and evidence

We use the strongest evidence available and say which kind it is.

Proposed offering

Evidence status for this service

This is a service Kindlebit proposes to deliver. No named customer project is published for it on this site.

See case study status

Reference ArchitectureInteractive demo with simulated data

Shared AI platform with model gateway, logging and cost metering

The architecture is shown in the Pilot to Production page and used in the AI SaaS product demonstration. It is a reference design, not a described customer deployment.

Open the demonstration

Business outcomes and success criteria

These are the measures we would agree before work starts. They are criteria for success, not results from past engagements.

Platform reuse

Share of AI projects built on shared components.

Adoption

Weekly active use by target role.

Cost control

Cost per completed task and budget variance.

Governance coverage

Share of live AI systems with an owner, risk tier and evaluation plan.

Questions buyers ask

How is a roadmap different from a strategy deck?

A deck states intent. The roadmap assigns owners, sequences funded work, defines architecture and sets the measures used to continue or stop each initiative.

How long does it take?

A roadmap for one business unit commonly takes six to ten weeks. Enterprise wide programmes take longer. We confirm timing after we know how many stakeholders and systems are in scope.

Do you tie us to specific vendors?

No. We compare options against your cloud, security and cost constraints. Where we recommend a build, the reasons and alternatives are documented.

How do you handle regulation that is still changing?

We design controls around principles that hold across jurisdictions, such as risk tiering, human oversight, logging and documentation, and schedule a review point so policy can be updated as rules change.

Can you help us execute afterwards?

Yes. The roadmap is written so any capable team can execute it. If you want Kindlebit to build, the first wave is scoped as a pilot with success criteria.

How do you control ongoing cost?

By metering at the gateway, budgeting per team, routing simple tasks to cheaper models and setting context limits. These are design requirements in the platform plan, not an afterthought.

Related services

Industries where this applies

Put your AI programme under one plan.

Share where your teams are today. We will outline the operating model, platform and sequence that would bring them together.

Discuss your AI roadmap Explore a demonstration